import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				os.path.join(args.tf_model_dir				,     '''parameters.json'''				)
				__lowercase					     =				json.loads(open(lowercase				).read()				)
				if not params:
								raise ValueError(
								    F"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."				)
				if not args.output.endswith('''.pt'''				):
								__lowercase					     =				args.output + '''.pt'''
				__lowercase					     =				OrderedDict()
				with tf.device('''/CPU:0'''				):
								__lowercase					     =				tf.train.load_checkpoint(args.tf_model_dir				)
								__lowercase					     =				reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												__lowercase					     =				reader.get_tensor(lowercase				).astype(np.floataa				)
												if key_name.endswith('''/adam_m'''				) or key_name.endswith('''/adam_v'''				):
																continue
												if key_name.startswith('''pasts/'''				):
																if key_name.startswith('''pasts/mlp'''				):
																				__lowercase					     =				int(key_name[9]				)
																elif key_name.startswith('''pasts/out'''				):
																				__lowercase					     =				8
																__lowercase					     =				'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																__lowercase					     =				vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
																__lowercase					     =				torch.tensor(lowercase				)
												elif key_name.startswith('''model/moe'''				):
																__lowercase					     =				int(key_name[9:].split('''/'''				)[0]				)
																if key_name.endswith('''/switch_gating/kernel'''				):
																				__lowercase					     =				'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
																				__lowercase					     =				vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
																				__lowercase					     =				torch.tensor(lowercase				)
																elif key_name.endswith('''/softmlp/kernel'''				):
																				__lowercase					     =				'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
																				__lowercase					     =				vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
																				__lowercase					     =				torch.tensor(lowercase				)
																elif key_name.endswith('''/wo/kernel'''				) or key_name.endswith('''/wi/kernel'''				):
																				__lowercase					     =				key_name[-9:-7]
																				for i in range(16				):
																								__lowercase					     =				'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
																								__lowercase					     =				(
																								    vnp[i].transpose([1, 0]				).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								__lowercase					     =				torch.tensor(lowercase				)
												elif key_name.startswith('''model/mlp'''				):
																__lowercase					     =				int(key_name[9:].split('''/'''				)[0]				)
																if key_name.endswith('''/p1/kernel'''				):
																				__lowercase					     =				'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
																				__lowercase					     =				vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
																				__lowercase					     =				torch.tensor(lowercase				)
																elif key_name.endswith('''/p1/bias'''				):
																				__lowercase					     =				'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
																				__lowercase					     =				vnp.copy()  # same because it is one dimensional
																				__lowercase					     =				torch.tensor(lowercase				)
																elif key_name.endswith('''/p2/kernel'''				):
																				__lowercase					     =				'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
																				__lowercase					     =				vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
																				__lowercase					     =				torch.tensor(lowercase				)
																elif key_name.endswith('''/p2/bias'''				):
																				__lowercase					     =				'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
																				__lowercase					     =				vnp.copy()  # same because it is one dimensional
																				__lowercase					     =				torch.tensor(lowercase				)
												elif key_name.startswith('''model/ln'''				):
																__lowercase					     =				int(key_name[8:].split('''/'''				)[0]				)
																if key_name.endswith('''/b'''				):
																				__lowercase					     =				'''model.blocks.%d.feed_forward.norm.bias''' % player
																				__lowercase					     =				vnp.copy()  # same because it is one dimensional
																				__lowercase					     =				torch.tensor(lowercase				)
																elif key_name.endswith('''/g'''				):
																				__lowercase					     =				'''model.blocks.%d.feed_forward.norm.weight''' % player
																				__lowercase					     =				vnp.copy()  # same because it is one dimensional
																				__lowercase					     =				torch.tensor(lowercase				)
												elif key_name.startswith('''model/att'''				):
																__lowercase					     =				int(key_name[9:].split('''/'''				)[0]				)
																if key_name.endswith('''/qkv/kernel'''				):
																				__lowercase					     =				vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				__lowercase					     =				state[:, 0, :, :]
																				__lowercase					     =				state[:, 1, :, :]
																				__lowercase					     =				state[:, 2, :, :]
																				__lowercase					     =				(
																				    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]				)
																				    .transpose([1, 0]				)
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				__lowercase					     =				(
																				    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]				)
																				    .transpose([1, 0]				)
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				__lowercase					     =				(
																				    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]				)
																				    .transpose([1, 0]				)
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				__lowercase					     =				'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
																				__lowercase					     =				torch.tensor(lowercase				)
																				__lowercase					     =				'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
																				__lowercase					     =				torch.tensor(lowercase				)
																				__lowercase					     =				'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
																				__lowercase					     =				torch.tensor(lowercase				)
																elif key_name.endswith('''/o/kernel'''				):
																				__lowercase					     =				'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
																				__lowercase					     =				(
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]				).transpose([1, 0]				).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				__lowercase					     =				torch.tensor(lowercase				)
												elif key_name.startswith('''model/an'''				):
																__lowercase					     =				int(key_name[8:].split('''/'''				)[0]				)
																if key_name.endswith('''/b'''				):
																				__lowercase					     =				'''model.blocks.%d.self_attn.norm.bias''' % player
																				__lowercase					     =				vnp.copy()  # same because it is one dimensional
																				__lowercase					     =				torch.tensor(lowercase				)
																elif key_name.endswith('''/g'''				):
																				__lowercase					     =				'''model.blocks.%d.self_attn.norm.weight''' % player
																				__lowercase					     =				vnp.copy()  # same because it is one dimensional
																				__lowercase					     =				torch.tensor(lowercase				)
												elif (
												    key_name.startswith('''model/wte'''				)
												    or key_name.startswith('''model/wpe'''				)
												    or key_name.startswith('''model/ete'''				)
												):
																__lowercase					     =				{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
																    key_name[-3:]
																]
																__lowercase					     =				'''model.%s.weight''' % nlayer
																__lowercase					     =				vnp.copy()  # same in embedded
																__lowercase					     =				torch.tensor(lowercase				)
																if key_name.startswith('''model/wte'''				):
																				__lowercase					     =				'''lm_head.weight'''
																				__lowercase					     =				vnp.copy()  # same in embedded
																				__lowercase					     =				torch.tensor(lowercase				)
												elif key_name.startswith('''model/wob'''				):
																__lowercase					     =				'''final_logits_bias'''
																__lowercase					     =				vnp.copy()  # same in embedded
																__lowercase					     =				state.reshape((1, -1)				)
																__lowercase					     =				torch.tensor(lowercase				)
												elif key_name == "model/dense/kernel":
																__lowercase					     =				'''model.last_project.weight'''
																__lowercase					     =				vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
																__lowercase					     =				torch.tensor(lowercase				)
												elif key_name == "model/dense_1/bias":
																__lowercase					     =				'''model.last_project.bias'''
																__lowercase					     =				vnp.copy()  # same because it is one dimensional
																__lowercase					     =				torch.tensor(lowercase				)
				torch.save(lowercase				,     args.output				)


if __name__ == "__main__":
		__a    :	List[str]         =    argparse.ArgumentParser(
		    description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
		parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
		__a    :	List[str]         =    parser.parse_args()
		convert_tf_gptsan_to_pt(args)
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a    :	int         =    logging.get_logger(__name__)

__a    :	Optional[int]         =    {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Any     					=		'''pegasus'''
			__a    :					Optional[Any]     					=		['''past_key_values''']
			__a    :					Tuple     					=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





			def __init__(  self ,  lowerCAmelCase__=5_02_65 ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=40_96 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=40_96 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=False ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=1 ,  lowerCAmelCase__=1 ,  **lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							__lowercase					     =				vocab_size
							__lowercase					     =				max_position_embeddings
							__lowercase					     =				d_model
							__lowercase					     =				encoder_ffn_dim
							__lowercase					     =				encoder_layers
							__lowercase					     =				encoder_attention_heads
							__lowercase					     =				decoder_ffn_dim
							__lowercase					     =				decoder_layers
							__lowercase					     =				decoder_attention_heads
							__lowercase					     =				dropout
							__lowercase					     =				attention_dropout
							__lowercase					     =				activation_dropout
							__lowercase					     =				activation_function
							__lowercase					     =				init_std
							__lowercase					     =				encoder_layerdrop
							__lowercase					     =				decoder_layerdrop
							__lowercase					     =				use_cache
							__lowercase					     =				encoder_layers
							__lowercase					     =				scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=lowerCAmelCase__ ,  eos_token_id=lowerCAmelCase__ ,  is_encoder_decoder=lowerCAmelCase__ ,  decoder_start_token_id=lowerCAmelCase__ ,  forced_eos_token_id=lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return self.encoder_attention_heads





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return self.d_model